Data Analytics Simplified
Welcome to Data Analytics Simplified, a blog dedicated to helping you streamline data workflows, automate processes, and scale your infrastructure—without the headaches. Whether you’re battling messy spreadsheets, inefficient pipelines, or trying to get the most out of your data analytics investments, you’re in the right place.
I’ll share proven strategies, tips, and frameworks from my experience in data engineering and analytics, focusing on:
Data doesn’t have to be overwhelming. With the right approach, you can declutter, optimize, and build a solid foundation for data science and analytics.
Let’s get to work.
Here is how you can return multiple pandas columns from an apply function.
You’ll need to run the following commands to download a Plotly Express chart as an image when using Google Colab.
If you need to send a file to an SFTP server, you can easily do that with Python. In this post, I’ll show you how.
In this post, I’ll show you how to apply a forward fill using the ffill() function in pandas and only apply the transformation to a specified grouping.
In this post, I’ll show you three methods to remove or prevent duplicate columns when merging two DataFrames.
In this post, I’ll take a look at the data from Our World in Data to visualize data from the COVID-19 vaccine rollout.
If you have a long-running query, splitting it up into smaller queries can help with performance. With Python, we can dynamically loop through each query.
In this post, I’ll show you how to return the common list items between two lists in Python.